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Strong employer brand, popular Data Engineer title, and metro location increase applicant competition.
Core data engineering skills are transferable, but preferred financial-products experience raises domain specificity.
Requires specific data stack (SQL, Python, Databricks) and financial services experience but no explicit years requirement.
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Build and optimize data pipelines and data models primarily using SQL and Python.
Develop and deliver data solutions that support business analytics and AI initiatives, including deployment and maintenance of AI agents or models.
Work with cloud-based data platforms like Databricks to enable data extraction, transformation, and automation.
Proficiency in SQL and Python; knowledge of PySpark is an advantage.
Experience with Databricks or similar cloud-based data platforms.
Work Experience Required: Experience in data engineering or analytics within financial services, preferably with credit card or personal loan product exposure.
Preferred: Databricks Data Engineer certification; familiarity with SAS and Power BI is a plus.
Experience delivering data engineering solutions supporting business analytics and AI in financial services domain.
Ability to work independently and collaboratively on complex data projects involving cloud platforms and AI deployment.
Strong problem-solving skills with attention to detail and effective stakeholder communication.